Citation

Umair, Muhammad and Ahmad, Jawad and Alasbali, Nada and Saidani, Oumaima and Hanif, Muhammad and Khattak, Aizaz Ahmad and Khan, Muhammad Shahbaz (2025) Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning. Frontiers in Computational Neuroscience, 19. ISSN 1662-5188

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Abstract

Introduction: Major Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disorders. However, the inherent complexity of EEG signals along with the human error in interpreting these readings requires the need for more reliable, automated methods of detection. Methods: This study utilizes EEG signals to classify MDD and healthy individuals through a combination of machine learning, deep learning, and split learning approaches. State of the art machine learning models i.e., Random Forest, Support Vector Machine, and Gradient Boosting are utilized, while deep learning models such as Transformers and Autoencoders are selected for their robust feature-extraction capabilities. Traditional methods for training machine learning and deep learning models raises data privacy concerns and require significant computational resources. To address these issues, the study applies a split learning framework. In this framework, an ensemble learning technique has been utilized that combines the best performing machine and deep learning models. Results: Results demonstrate a commendable classification performance with certain ensemble methods, and a Transformer-Random Forest combination achieved 99% accuracy. In addition, to address data-sharing constraints, a split learning framework is implemented across three clients, yielding high accuracy (over 95%) while preserving privacy. The best client recorded 96.23% accuracy, underscoring the robustness of combining Transformers with Random Forest under resource-constrained conditions. Discussion: These findings demonstrate that distributed deep learning pipelines can deliver precise MDD detection from EEG data without compromising data security. Proposed framework keeps data on local nodes and only exchanges intermediate representations. This approach meets institutional privacy requirements while providing robust classification outcomes.

Item Type:	Article
Uncontrolled Keywords:	split learning, transformers, autoencoder, EEG, major depressive disorder, smart diagnostic, neurological behavior
Subjects:	Q Science > QC Physics > QC501-766 Electricity and magnetism > QC501-(721) Electricity > QC669-675.8 Electromagnetic theory
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	28 May 2025 01:13
Last Modified:	28 May 2025 01:13
URII:	http://shdl.mmu.edu.my/id/eprint/13849

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